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Processes
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Neural Computation and Applications for Sustainable Energy Systems (II)
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Neural Computation and Applications for Sustainable Energy Systems (II)

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 4609

Special Issue Editors

Prof. Dr. Giacomo Capizzi

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Guest Editor
UNICT, Department of Electrical, Electronics and Informatics Engineering (DIEEI), University of Catania, 95125 Catania, Italy
Interests: neural networks; wavelet theory; statistical pattern recognition; Bayesian networks; integrated generation systems; renewable energy sources; battery storage modeling and simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Grazia Lo Sciuto

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Guest Editor
UNICT, Department of Electrical, Electronics and Informatics Engineering (DIEEI), University of Catania, 95125 Catania, Italy
Interests: neural networks; electronic devices; organic solar cells; photovoltaic; renewable energy; renewable energy sources; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Di Nunzio

E-Mail Website
Guest Editor
Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
Interests: FPGA; ASIC; machine learning; digital signal processing; embedded systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

In the last few years, renewable energy sources, for example, solar, wind, hydroelectric, and geothermal, have been increasingly utilized in integrated generation systems (IGSs) and smart grids (SGs). Due to their intermittent and seasonal nature, such generation systems do not provide a continuous source of energy, and cannot satisfy constant power demand due to significant fluctuations in the magnitude of power supplied.

The use of Machine Learning and Deep Learning represents an interesting solution for the power's trend forecasting and power control in order to mitigate the negative effects of these power fluctuations. Convolution neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM) play important roles in the realization of high-efficiency renewable energy systems.

This Special Issue is focused on the application of neural computation techniques to renewable power system operations, such as time-series energy forecasting, renewable energy markets, energy storage systems (ESS), microgrids, and distribution networks. Possible submissions are not limited to these topics, and papers related to intelligent methods of sustainable energy systems are also welcome. Potential topics include, but are not limited to, the following:

  • Applications of intelligent methods to smart grids;
  • Energy management in photovoltaic plants that makes use of neural predictions;
  • Intelligent energy management in wind farms;
  • Optimal energy dispatch management using neural network (intelligent) predictors;
  • Optimal management of various renewable energy sources;
  • Cloud-based energy management in intelligent smart grids.
  • Intelligent IoT systems applied to renewable energy systems;
  • Hardware and software implementation of intelligent energy management systems;

Prof. Dr. Giacomo Capizzi
Dr. Grazia Lo Sciuto
Dr. Luca Di Nunzio
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neural networks
  • renewable energy
  • energy management
  • photovoltaic plants
  • wind farm
  • fuzzy logic
  • genetic algorithms
  • cloud computing
  • nature-inspired predictions methods

Published Papers (2 papers)

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Research

21 pages, 1427 KiB  
Article
Non-Intrusive Load Monitoring Based on Deep Pairwise-Supervised Hashing to Detect Unidentified Appliances
by Qiang Zhao, Yao Xu, Zhenfan Wei and Yinghua Han
Processes 2021, 9(3), 505; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9030505 - 11 Mar 2021
Cited by 11 | Viewed by 1790
Abstract
Non-intrusive load monitoring (NILM) is a fast developing technique for appliances operation recognition in power system monitoring. At present, most NILM algorithms rely on the assumption that all fluctuations in the data stream are triggered by identified appliances. Therefore, NILM of identifying unidentified [...] Read more.
Non-intrusive load monitoring (NILM) is a fast developing technique for appliances operation recognition in power system monitoring. At present, most NILM algorithms rely on the assumption that all fluctuations in the data stream are triggered by identified appliances. Therefore, NILM of identifying unidentified appliances is still an open challenge. To pursue a scalable solution to energy monitoring for contemporary unidentified appliances, we propose a voltage-current (V-I) trajectory enabled deep pairwise-supervised hashing (DPSH) method for NILM. DPSH performs simultaneous feature learning and hash-code learning with deep neural networks, which shows higher identification accuracy than a benchmark method. DPSH can generate different hash codes to distinguish identified appliances. For unidentified appliances, it generates completely new codes that are different from codes of multiple identified appliances to distinguish them. Experiments on public datasets show that our method can get better F1-score than the benchmark method to achieve state-of-the-art performance in the identification of unidentified appliances, and this method maintains high sustainability to identify other unidentified appliances through retraining. DPSH can be resilient against appliance changes in the house. Full article
(This article belongs to the Special Issue Neural Computation and Applications for Sustainable Energy Systems (II))
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20 pages, 4300 KiB  
Article
Nexus between Energy Usability, Economic Indicators and Environmental Sustainability in Four ASEAN Countries: A Non-Linear Autoregressive Exogenous Neural Network Modelling Approach
by Siti Indati Mustapa, Freida Ozavize Ayodele, Bamidele Victor Ayodele and Norsyahida Mohammad
Processes 2020, 8(12), 1529; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8121529 - 25 Nov 2020
Cited by 8 | Viewed by 1652
Abstract
This study investigates the use of a non-linear autoregressive exogenous neural network (NARX) model to investigate the nexus between energy usability, economic indicators, and carbon dioxide (CO2) emissions in four Association of South East Asian Nations (ASEAN), namely Malaysia, Thailand, Indonesia, [...] Read more.
This study investigates the use of a non-linear autoregressive exogenous neural network (NARX) model to investigate the nexus between energy usability, economic indicators, and carbon dioxide (CO2) emissions in four Association of South East Asian Nations (ASEAN), namely Malaysia, Thailand, Indonesia, and the Philippines. Optimized NARX model architectures of 5-29-1, 5-19-1, 5-17-1, 5-13-1 representing the input nodes, hidden neurons and the output units were obtained from the series of models configured. Based on the relationship between the input variables, CO2 emissions were predicted with a high correlation coefficient (R) > 0.9. and low mean square errors (MSE) of 3.92 × 10−21, 4.15 × 10−23, 2.02 × 10−19, 1.32 × 10−20 for Malaysia, Thailand, Indonesia, and the Philippines, respectively. Coal consumption has the highest level of influence on CO2 emissions in the four ASEAN countries based on the sensitivity analysis. These findings suggest that government policies in the four ASEAN countries should be more intensified on strategies to reduce CO2 emissions in relationship with the energy and economic indicators. Full article
(This article belongs to the Special Issue Neural Computation and Applications for Sustainable Energy Systems (II))
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